In this issue, Chong et al.1 describe and validate a novel method to take the impenetrable mass of data generated by the 80,000 or so breaths/day recorded in a ventilator’s memory, organize it, categorize it, query it, and study it. They do this by their development of special computing techniques that greatly expand the algorithms provided by ventilator manufacturers, using the raw pressure and flow data captured at 100 Hz and re-creating the individual breaths in all their complexity. They can generate loops and waveforms from individual breaths or consolidate the data into time-based intervals, breath types, and provide information about the sub-phases of each breath; for example, initiation as spontaneous or ventilator derived, duration of inspiratory flow, presence or absence of an inspiratory hold, asynchrony, and many other combinations. A 24-h sample can be analyzed for evaluation in 2 min. The authors also studied data not used for algorithm development to compare the accuracy of the algorithm to 3 samples of 5 min each that were hand-analyzed and collated by specially trained physician. They demonstrate how a 24-h sample may be broken down into intervals occurring around specific events for better understanding of what happened. Finally, this program, called Ventiliser, is open sourced, freely available, and usable on any platform that allows data to be downloaded from the ventilator in use.

Understanding the interaction of the sick neonate requiring respiratory support with the devices we use to provide it has been at times opaque, at times through a glass darkly, but rarely clear. Smith et al. published an early effort to shed light on what happens during mechanical ventilation in this journal in 1969.2 This study of 6 infants of roughly 2000 g with respiratory distress syndrome (RDS) used controlled ventilation with paralysis and varied respiratory rate and peak inspiratory pressure. They found that PaO2 varied directly with pressure and indirectly with rate and concluded that mechanical ventilation might be useful in the treatment of RDS. Over the next decade, we learned about positive end-expiratory pressure,3 mean airway pressure,4 and the potentially harmful interactions between an infant breathing spontaneously but out of phase with the ventilator and cerebral blood flow.5 This new worry resulted in techniques to allow the infant to synchronize with the ventilator-delivered breaths, first by trying to “capture” the infant into the phase of the ventilator, then by actual synchronization.6,7 During the 1980s, technology was developed that added the newly developed personal computer to graphically present information obtained on a breath-to-breath cycle at the bedside.8 This was revolutionary as there had never been reliable techniques that could be applied outside of a laboratory setting to study infants in real time during acute illness. This technique was used in a number of studies over the years but had the limitation of being only intermittently available as it was a stand-alone device, rolled from patient to patient for sampling of a few breaths.9,10,11,12,13

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